AI for Monitoring & Evaluation Professionals

Course overview

AI for Monitoring & Evaluation Professionals is a practical training course designed to equip M&E officers, project managers, researchers, NGO professionals, development practitioners, and data analysts with the skills to use Artificial Intelligence to improve programme monitoring, evaluation, data collection, analysis, reporting, and decision-making. Participants learn how to apply Generative AI and AI-powered tools to develop indicators, logframes and theories of change, design questionnaires, analyse qualitative and quantitative data, identify trends and anomalies, generate reports, visualise programme performance, and communicate evidence to stakeholders. The course also addresses responsible AI use, data privacy, validation of AI-generated outputs, and ethical considerations in development and evaluation work.

Course objectives

• Course Objectives By the end of the course, participants will be able to: Apply AI throughout the M&E project lifecycle. Use Generative AI to develop logframes, theories of change and M&E frameworks. Develop indicators, targets and data-collection tools using AI. Use AI to improve questionnaires and survey instruments. Analyse quantitative and qualitative M&E data using AI. Identify trends, anomalies and potential data-quality problems. Use AI to generate programme-performance summaries. Apply AI to evaluation design and interpretation. Produce M&E reports, dashboards and executive summaries more efficiently. Combine AI with Excel, Power BI, KoboToolbox and other data-analysis tools. Validate AI outputs and maintain professional judgement. Apply responsible AI, data-protection and ethical principles in M&E.

Who should attend

Course content

• Module 1: Introduction to AI for M&E AI and Generative AI fundamentals Applications of AI in development programmes Opportunities and limitations AI-assisted versus traditional M&E workflows
• Module 2: AI for M&E Planning Developing M&E frameworks Logframes and results frameworks Theories of Change Developing indicators and targets Indicator definitions and measurement plans
• Module 3: AI for Data Collection Designing questionnaires with AI Survey questions and response options KoboToolbox/ODK questionnaire development Interview and FGD guides Data-collection protocols
• Module 4: AI for Data Quality Data validation Identifying missing and inconsistent data Detecting anomalies and outliers Data-cleaning support Data-quality assessment
• Module 5: AI for Quantitative Data Analysis Descriptive analysis Cross-tabulation Trend analysis Correlation and comparison Statistical interpretation AI-assisted Excel/SPSS analysis
• Module 6: AI for Qualitative Data Analysis Coding interview data Thematic analysis Sentiment and opinion analysis Identifying recurring themes Summarising interviews and focus groups
• Module 7: AI for Programme Performance Analysis Monitoring outputs and outcomes Comparing targets against achievements Performance-gap analysis Identifying programme risks Generating evidence-based insights
• Module 8: AI + Power BI for M&E Preparing M&E datasets AI-assisted data transformation Designing M&E dashboards KPIs and performance indicators Automated narratives and insights Communicating findings visually
• Module 9: AI for Evaluation Evaluation questions Evaluation designs Baseline and endline analysis Impact assessment Outcome analysis Interpretation of evaluation findings
• Module 10: AI for M&E Reporting Monthly and quarterly reports Donor reports Executive summaries Lessons learned Recommendations Presentation of findings
• Module 11: Prompt Engineering for M&E M&E-specific prompting Prompts for indicators and frameworks Prompts for data analysis Prompts for report writing Creating reusable M&E prompt libraries
• Module 12: Responsible AI in M&E Data privacy and confidentiality Protection of beneficiary information AI hallucinations Bias in AI-assisted analysis Verification and human oversight Ethical use of AI in development programmes

General notes

• Training is practical and hands-on, with demonstrations and exercises based on real-world Monitoring & Evaluation scenarios.
• Participants will work with Generative AI tools to support M&E planning, data collection, analysis, reporting, and programme decision-making.
• Training materials, practical exercises, examples, and reference materials will be provided. Participants are encouraged to bring a laptop with Microsoft Excel and, where possible, access to relevant AI tools.
• Participants should have basic knowledge of Monitoring & Evaluation, programme management, research, or data analysis. AI-generated outputs must be reviewed and validated by the user before being used for official reports, evaluations, or decisions.
• Participants will be guided on responsible handling of confidential, sensitive, and beneficiary-related information when using AI tools.
• The course focuses on AI-assisted M&E, meaning AI supports professional judgement rather than replacing M&E expertise.

Schedule & fees